1 research outputs found
Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes
Self-supervised depth estimators have recently shown results comparable to
the supervised methods on the challenging single image depth estimation (SIDE)
task, by exploiting the geometrical relations between target and reference
views in the training data. However, previous methods usually learn forward or
backward image synthesis, but not depth estimation, as they cannot effectively
neglect occlusions between the target and the reference images. Previous works
rely on rigid photometric assumptions or the SIDE network to infer depth and
occlusions, resulting in limited performance. On the other hand, we propose a
method to "Forget About the LiDAR" (FAL), for the training of depth estimators,
with Mirrored Exponential Disparity (MED) probability volumes, from which we
obtain geometrically inspired occlusion maps with our novel Mirrored Occlusion
Module (MOM). Our MOM does not impose a burden on our FAL-net. Contrary to the
previous methods that learn SIDE from stereo pairs by regressing disparity in
the linear space, our FAL-net regresses disparity by binning it into the
exponential space, which allows for better detection of distant and nearby
objects. We define a two-step training strategy for our FAL-net: It is first
trained for view synthesis and then fine-tuned for depth estimation with our
MOM. Our FAL-net is remarkably light-weight and outperforms the previous
state-of-the-art methods with 8x fewer parameters and 3x faster inference
speeds on the challenging KITTI dataset. We present extensive experimental
results on the KITTI, CityScapes, and Make3D datasets to verify our method's
effectiveness. To the authors' best knowledge, the presented method performs
the best among all the previous self-supervised methods until now.Comment: Accepted to NeurIPS202